File size: 11,655 Bytes
05e2d85 8632702 05e2d85 8632702 05e2d85 8632702 05e2d85 8632702 05e2d85 8632702 05e2d85 8632702 05e2d85 8632702 05e2d85 af2c1f1 05e2d85 8632702 05e2d85 8632702 05e2d85 8632702 af2c1f1 8632702 05e2d85 8632702 05e2d85 8632702 05e2d85 8632702 05e2d85 8632702 05e2d85 8632702 05e2d85 af2c1f1 05e2d85 af2c1f1 8632702 05e2d85 af2c1f1 05e2d85 af2c1f1 05e2d85 af2c1f1 05e2d85 af2c1f1 05e2d85 8632702 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 |
# From https://stackoverflow.com/a/23689767
# From https://github.com/pytorch/pytorch/issues/97899
# From https://github.com/facebookresearch/llama/blob/main/llama/model.py
import torch
from torch import nn
from torch.nn.functional import scaled_dot_product_attention
from xformers.ops import SwiGLU, memory_efficient_attention
from .rmsnorm import RMSNorm
from .rotary import precompute_freqs_cis, apply_rotary_emb
from transformers import PreTrainedModel, PretrainedConfig
from transformers.modeling_outputs import MaskedLMOutput
class DotDict(dict):
"""Dictionary that supports the dot notation to access attributes (similarly to HuggingFace)."""
__getattr__ = dict.get
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
class AMPLIFYConfig(PretrainedConfig):
model_type = "AMPLIFY"
# All config parameters must have a default value.
def __init__(
self,
hidden_size: int = 960,
num_hidden_layers: int = 32,
num_attention_heads: int = 15,
intermediate_size: int = 3840,
dropout_prob: float = 0,
embedding_init_range: float = 0.02,
decoder_init_range: float = 0.02,
rms_norm: bool = True,
norm_eps: float = 1e-05,
hidden_act: str = "SwiGLU",
layer_norm_after_embedding: bool = False,
layer_norm_before_last_layer: bool = True,
vocab_size: int = 27,
ffn_bias: bool = False,
att_bias: bool = False,
pad_token_id: int = 0,
max_length: int = 2048,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.dropout_prob = dropout_prob
self.embedding_init_range = embedding_init_range
self.decoder_init_range = decoder_init_range
self.rms_norm = rms_norm
self.norm_eps = norm_eps
self.hidden_act = hidden_act
self.layer_norm_after_embedding = layer_norm_after_embedding
self.layer_norm_before_last_layer = layer_norm_before_last_layer
self.vocab_size = vocab_size
self.ffn_bias = ffn_bias
self.att_bias = att_bias
self.pad_token_id = pad_token_id
self.max_length = max_length
class EncoderBlock(nn.Module):
"""Transformer encoder block."""
def __init__(self, config: AMPLIFYConfig):
"""Initialize a EncoderBlock.
Args:
hidden_size (int): _description_
num_attention_heads (int): _description_
intermediate_size (int, optional): _description_. Defaults to 2048.
dropout_prob (float, optional): _description_. Defaults to 0.1.
activation (str, optional): _description_. Defaults to "relu".
rms_norm (bool, optional): _description_. Defaults to True.
norm_eps (float, optional): _description_. Defaults to 1e-5.
pad_token_id (int, optional): _description_. Defaults to 0.
max_length (int, optional): _description_. Defaults to 2048.
ffn_bias (bool, optional): _description_. Defaults to False.
att_bias (bool, optional): _description_. Defaults to False.
"""
super().__init__()
self.config = config
self.d_head = config.hidden_size // config.num_attention_heads
# Attention
self.q = nn.Linear(in_features=config.hidden_size, out_features=config.hidden_size, bias=config.att_bias)
self.k = nn.Linear(in_features=config.hidden_size, out_features=config.hidden_size, bias=config.att_bias)
self.v = nn.Linear(in_features=config.hidden_size, out_features=config.hidden_size, bias=config.att_bias)
self.wo = nn.Linear(in_features=config.hidden_size, out_features=config.hidden_size, bias=config.att_bias)
self.resid_dropout = nn.Dropout(config.dropout_prob)
# Feedforward network
match config.hidden_act.lower():
case "swiglu":
# To keep the number of parameters and the amount of computation constant, we reduce the number of
# hidden units by a factor of 2/3 (https://arxiv.org/pdf/2002.05202.pdf) and make it a multiple of 8 to
# avoid RuntimeError due to misaligned operand
multiple_of = 8
intermediate_size = int(2 * config.intermediate_size / 3)
intermediate_size = multiple_of * ((intermediate_size + multiple_of - 1) // multiple_of)
self.ffn = SwiGLU(config.hidden_size, intermediate_size, config.hidden_size, bias=config.ffn_bias)
case "relu":
self.ffn = nn.Sequential(
nn.Linear(config.hidden_size, config.intermediate_size, bias=config.ffn_bias),
nn.ReLU(),
nn.Linear(config.intermediate_size, config.hidden_size, bias=config.ffn_bias),
)
case "gelu":
self.ffn = nn.Sequential(
nn.Linear(config.hidden_size, config.intermediate_size, bias=config.ffn_bias),
nn.GELU(),
nn.Linear(config.intermediate_size, config.hidden_size, bias=config.ffn_bias),
)
self.attention_norm = (
RMSNorm(config.hidden_size, config.norm_eps)
if config.rms_norm
else nn.LayerNorm(config.hidden_size, config.norm_eps)
)
self.ffn_norm = (
RMSNorm(config.hidden_size, config.norm_eps)
if config.rms_norm
else nn.LayerNorm(config.hidden_size, config.norm_eps)
)
self.ffn_dropout = nn.Dropout(config.dropout_prob)
def forward(self, x: torch.Tensor, attention_mask: torch.Tensor, freqs_cis: torch.Tensor, output_attentions: bool):
attn, contact = self._att_block(self.attention_norm(x), attention_mask, freqs_cis, output_attentions)
x = x + attn
x = x + self._ff_block(self.ffn_norm(x))
return x, contact
def _att_block(
self, x: torch.Tensor, attention_mask: torch.Tensor, freqs_cis: torch.Tensor, output_attentions: bool
):
batch_size, seq_len, _ = x.shape
xq, xk, xv = self.q(x), self.k(x), self.v(x)
# Reshape for rotary embeddings
xq = xq.view(batch_size, seq_len, self.config.num_attention_heads, self.d_head)
xk = xk.view(batch_size, seq_len, self.config.num_attention_heads, self.d_head)
xv = xv.view(batch_size, seq_len, self.config.num_attention_heads, self.d_head)
xq, xk = apply_rotary_emb(xq, xk, freqs_cis)
# Compute the attention weight
attn_weights = None
if output_attentions:
attn_weights = xq.permute(0, 2, 1, 3) @ xk.permute(0, 2, 3, 1) / (xq.size(-1) ** 0.5)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
attn_weights = attn_weights.softmax(-1)
# Compute the attention using xformers if the tensors are on GPU
if x.is_cuda:
# Input and output are of dimension (B, M, H, K) where B is the batch size, M the sequence length,
# H the number of heads, and K the embeding size per head
attn = memory_efficient_attention(
query=xq,
key=xk,
value=xv,
attn_bias=attention_mask,
p=self.config.dropout_prob if self.training else 0,
)
else:
# Input and output are of dimension (B, H, M, K)
attn = scaled_dot_product_attention(
query=xq.transpose(1, 2),
key=xk.transpose(1, 2),
value=xv.transpose(1, 2),
attn_mask=attention_mask,
dropout_p=self.config.dropout_prob if self.training else 0,
).transpose(1, 2)
attn_scores = self.wo(attn.view(batch_size, seq_len, self.config.num_attention_heads * self.d_head))
return (self.resid_dropout(attn_scores), attn_weights)
def _ff_block(self, x: torch.Tensor):
return self.ffn_dropout(self.ffn(x))
class AMPLIFYPreTrainedModel(PreTrainedModel):
config_class = AMPLIFYConfig
def _init_weights(self, module):
if isinstance(module, nn.Linear):
module.weight.data.uniform_(-self.config.decoder_init_range, self.config.decoder_init_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.uniform_(-self.config.embedding_init_range, self.config.embedding_init_range)
class AMPLIFY(AMPLIFYPreTrainedModel):
"""The main model class.
Args:
config (amplify.model.amplify.AMPLIFYConfig): model configuration, usually defined from the Hydra configuration.
"""
def __init__(self, config: AMPLIFYConfig, **kwargs):
super().__init__(config)
self.config = config
self.encoder = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
if config.layer_norm_after_embedding:
self.layer_norm_1 = (
RMSNorm(config.hidden_size, config.norm_eps)
if config.rms_norm
else nn.LayerNorm(config.hidden_size, config.norm_eps)
)
self.transformer_encoder = nn.ModuleList()
for _ in range(config.num_hidden_layers):
self.transformer_encoder.append(EncoderBlock(config))
if config.layer_norm_before_last_layer:
self.layer_norm_2 = (
RMSNorm(config.hidden_size, config.norm_eps)
if config.rms_norm
else nn.LayerNorm(config.hidden_size, config.norm_eps)
)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
self.freqs_cis = precompute_freqs_cis(config.hidden_size // config.num_attention_heads, config.max_length)
# Initialize weights and apply final processing
self.post_init()
def forward(self, input_ids, attention_mask=None, output_hidden_states=False, output_attentions=False, **kwargs):
# Initialize
hidden_states, attentions = [], []
# Expand and repeat: (Batch, Length) -> (Batch, Heads, Length, Length)
if attention_mask is not None and not torch.all(attention_mask == 0):
attention_mask = (
attention_mask.unsqueeze(1)
.unsqueeze(1)
.repeat(1, self.config.num_attention_heads, attention_mask.size(-1), 1)
)
else:
attention_mask = None
# RoPE
self.freqs_cis = self.freqs_cis.to(input_ids.device, non_blocking=True)
freqs_cis = self.freqs_cis[: input_ids.shape[1]]
# Embedding
x = self.encoder(input_ids)
if self.config.layer_norm_after_embedding:
x = self.layer_norm_1(x)
# Transformer encoder
for layer in self.transformer_encoder:
x, attn = layer(x, attention_mask, freqs_cis, output_attentions)
if output_hidden_states:
hidden_states.append(x)
if output_attentions:
attentions.append(attn)
# Classification head with layer norm
logits = self.decoder(self.layer_norm_2(x) if self.config.layer_norm_before_last_layer else x)
# Return logits or the output of the last hidden layer
return MaskedLMOutput(logits=logits, hidden_states=hidden_states, attentions=attentions)
|