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8ba58fb | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 | """Standalone AMPLIFY model for HuggingFace Hub (trust_remote_code=True).
This is a self-contained file that can be shipped in a HuggingFace repo so that
``AutoModel.from_pretrained(..., trust_remote_code=True)`` works without
installing the ``amplify`` package.
Based on: https://github.com/chandar-lab/AMPLIFY
"""
from typing import Tuple
import torch
from torch import nn
from torch.nn.functional import scaled_dot_product_attention
from transformers import PreTrainedModel, PretrainedConfig
from transformers.modeling_outputs import MaskedLMOutput
# Optional: flash attention for packed-sequence training. Not required for
# standard inference.
try:
from flash_attn.flash_attn_interface import flash_attn_varlen_func # type: ignore
except ImportError:
flash_attn_varlen_func = None
# ---------------------------------------------------------------------------
# Rotary positional embeddings (inlined from amplify.model.rotary)
# ---------------------------------------------------------------------------
def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
t = torch.arange(end, device=freqs.device, dtype=torch.float32)
freqs = torch.outer(t, freqs)
freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64
return freqs_cis
def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
assert freqs_cis.shape == (x.shape[0], x.shape[1], x.shape[-1])
return freqs_cis.contiguous().unsqueeze(2)
def apply_rotary_emb(
xq: torch.Tensor,
xk: torch.Tensor,
freqs_cis: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
freqs_cis = reshape_for_broadcast(freqs_cis, xq_)
xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
return xq_out.type_as(xq), xk_out.type_as(xk)
# ---------------------------------------------------------------------------
# Config
# ---------------------------------------------------------------------------
class AMPLIFYConfig(PretrainedConfig):
model_type = "AMPLIFY"
def __init__(
self,
hidden_size: int = 960,
num_hidden_layers: int = 32,
num_attention_heads: int = 15,
intermediate_size: int = 3840,
embedding_init_range: float = 0.02,
decoder_init_range: float = 0.02,
norm_eps: float = 1e-05,
vocab_size: int = 32,
pad_token_id: int = 0,
max_length: int = 2048,
max_protein_length: int = 50000,
**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.embedding_init_range = embedding_init_range
self.decoder_init_range = decoder_init_range
self.norm_eps = norm_eps
self.vocab_size = vocab_size
self.pad_token_id = pad_token_id
self.max_length = max_length
self.max_protein_length = max_protein_length
# ---------------------------------------------------------------------------
# Encoder blocks
# ---------------------------------------------------------------------------
class EncoderBlock(nn.Module):
"""Standard transformer encoder block with SwiGLU FFN and RoPE."""
def __init__(self, config: AMPLIFYConfig):
super().__init__()
self.config = config
self.d_head = config.hidden_size // config.num_attention_heads
# Attention
self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=False)
self.wo = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
# SwiGLU FFN
multiple_of = 8
intermediate_size = multiple_of * (
(int(2 * config.intermediate_size / 3) + multiple_of - 1) // multiple_of
)
self.c_fc = nn.Linear(config.hidden_size, 2 * intermediate_size, bias=False)
self.silu = nn.SiLU()
self.mlp_c_proj = nn.Linear(intermediate_size, config.hidden_size, bias=False)
self.attention_norm = nn.RMSNorm(config.hidden_size, config.norm_eps)
self.ffn_norm = nn.RMSNorm(config.hidden_size, config.norm_eps)
def forward(
self,
x: torch.Tensor,
attention_mask: torch.Tensor,
freqs_cis: torch.Tensor,
output_attentions: bool,
max_seqlen: int = None,
cu_seqlens: torch.Tensor = None,
):
batch_size, seq_len, _ = x.shape
xq, xk, xv = (
self.qkv(self.attention_norm(x))
.reshape(batch_size, seq_len, self.config.num_attention_heads, self.d_head * 3)
.chunk(3, axis=-1)
)
xq, xk = apply_rotary_emb(xq, xk, freqs_cis)
attn_weights = None
if cu_seqlens is not None:
assert flash_attn_varlen_func is not None, (
"flash_attn is required for packed-sequence attention. "
"Install with: pip install flash-attn"
)
attn = flash_attn_varlen_func(
q=xq.squeeze(0),
k=xk.squeeze(0),
v=xv.squeeze(0),
cu_seqlens_q=cu_seqlens.squeeze(),
cu_seqlens_k=cu_seqlens.squeeze(),
max_seqlen_q=max_seqlen,
max_seqlen_k=max_seqlen,
dropout_p=0.0,
causal=False,
)
elif 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)
attn = attn_weights @ xv.permute(0, 2, 1, 3)
attn = attn.transpose(1, 2)
else:
attn = scaled_dot_product_attention(
query=xq.transpose(1, 2),
key=xk.transpose(1, 2),
value=xv.transpose(1, 2),
attn_mask=attention_mask.bool() if attention_mask is not None else None,
dropout_p=0,
).transpose(1, 2)
attn = self.wo(attn.reshape(batch_size, seq_len, self.config.num_attention_heads * self.d_head))
x = x + attn
uv = self.c_fc(self.ffn_norm(x))
u, v = torch.chunk(uv, 2, dim=-1)
x_mlp = u * self.silu(v)
h_mlp = self.mlp_c_proj(x_mlp)
x = x + h_mlp
return x, attn_weights
# ---------------------------------------------------------------------------
# Model
# ---------------------------------------------------------------------------
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
)
elif isinstance(module, nn.Embedding):
module.weight.data.uniform_(
-self.config.embedding_init_range, self.config.embedding_init_range
)
class AMPLIFY(AMPLIFYPreTrainedModel):
"""AMPLIFY protein language model.
A transformer encoder for protein sequences using RoPE and SwiGLU,
trained with masked language modelling.
"""
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
)
self.transformer_encoder = nn.ModuleList()
for _ in range(config.num_hidden_layers):
self.transformer_encoder.append(EncoderBlock(config))
self.layer_norm = nn.RMSNorm(config.hidden_size, config.norm_eps)
self.decoder = nn.Linear(config.hidden_size, config.vocab_size)
freqs_cis = precompute_freqs_cis(
config.hidden_size // config.num_attention_heads,
config.max_protein_length * 2,
)
self.register_buffer("freqs_cis", freqs_cis, persistent=False)
self.post_init()
def forward(
self,
input_ids: torch.Tensor,
position_ids: torch.Tensor = None,
max_seqlen: int = None,
cu_seqlens: torch.Tensor = None,
attention_mask: torch.Tensor = None,
output_hidden_states: bool = False,
output_attentions: bool = False,
):
hidden_states, attentions = [], []
if isinstance(output_hidden_states, bool) and not output_hidden_states:
output_hidden_index = self.config.num_hidden_layers + 1
elif isinstance(output_hidden_states, int):
output_hidden_index = output_hidden_states
else:
output_hidden_index = 0
if attention_mask is not None:
attention_mask = (
attention_mask.unsqueeze(1)
.unsqueeze(1)
.repeat(1, self.config.num_attention_heads, attention_mask.size(-1), 1)
)
if cu_seqlens is not None:
assert not output_attentions, "Output attentions is not supported when sequences are packed."
assert max_seqlen is not None, "Missing max_seqlen. It must be provided when cu_seqlens are not None."
assert input_ids.shape[0] == 1, "Cumulative sequence lengths are provided but input_ids are not packed."
assert input_ids.is_cuda, "Packing uses flash-attention and is only supported on GPU."
# RoPE
if position_ids is not None:
freqs_cis = self.freqs_cis[position_ids]
else:
freqs_cis = (
self.freqs_cis[: input_ids.shape[1]]
.unsqueeze(0)
.repeat(input_ids.shape[0], 1, 1)
)
x = self.encoder(input_ids)
for idx, layer in enumerate(self.transformer_encoder):
x, attn = layer(x, attention_mask, freqs_cis, output_attentions, max_seqlen, cu_seqlens)
if idx >= output_hidden_index:
hidden_states.append(x)
if output_attentions:
attentions.append(attn)
logits = self.decoder(self.layer_norm(x))
return MaskedLMOutput(
logits=logits,
hidden_states=hidden_states,
attentions=attentions,
)
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