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ad7c73d 3817cd0 ad7c73d | 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 | """Portable ARLoop inference: PyTorch SDPA and FP32 attention-residual routing.
Adapted from ARLoop's model and Parcae building blocks; see LICENSE-MIT.
No training repository, FLA, Triton, or flash-attn installation is needed.
"""
import torch
from torch import nn
from torch.nn import functional as F
from transformers import PreTrainedModel, GenerationMixin
from transformers.cache_utils import DynamicCache
from transformers.modeling_outputs import CausalLMOutputWithPast
from .configuration_arloop import ARLoopConfig
class RMSNorm(nn.Module):
def __init__(self, size, eps):
super().__init__()
self.weight = nn.Parameter(torch.ones(size))
self.eps = eps
def forward(self, x):
# Preserve the native final norm's rounding and weight promotion.
y = x.float() * torch.rsqrt(x.float().square().mean(-1, keepdim=True) + self.eps)
return y.to(x.dtype) * self.weight
class Attention(nn.Module):
def __init__(self, config, layer):
super().__init__()
self.config = config
dim = config.hidden_size
kv_dim = config.num_key_value_heads * config.head_dim
self.c_q = nn.Linear(dim, dim, bias=False)
self.c_k = nn.Linear(dim, kv_dim, bias=False)
self.c_v = nn.Linear(dim, kv_dim, bias=False)
self.c_proj = nn.Linear(dim, dim, bias=False)
self.ve_gate = (nn.Linear(32, config.num_key_value_heads, bias=False)
if layer % 2 == (config.physical_layers - 1) % 2 else None)
def forward(self, x, positions, mask, value_embedding, cache, effective_layer):
c = self.config
batch, length, _ = x.shape
q = self.c_q(x).view(batch, length, c.num_attention_heads, c.head_dim)
k = self.c_k(x).view(batch, length, c.num_key_value_heads, c.head_dim)
v = self.c_v(x).view_as(k)
if self.ve_gate is not None:
gate = 2 * torch.sigmoid(self.ve_gate(x[..., :32]))
v = v + gate.unsqueeze(-1) * value_embedding.view_as(v).to(v.dtype)
# Adjacent-pair rotary embedding, unlike Llama's split-half convention.
freq = 1.0 / (c.rope_theta ** (torch.arange(0, c.head_dim, 2,
device=x.device, dtype=torch.float32) / c.head_dim))
angles = positions.float().unsqueeze(-1) / c.rope_condense_ratio * freq
cos, sin = angles.cos().unsqueeze(2), angles.sin().unsqueeze(2)
def rotate(tensor):
pairs = tensor.float().unflatten(-1, (-1, 2))
return torch.stack((pairs[..., 0] * cos - pairs[..., 1] * sin,
pairs[..., 1] * cos + pairs[..., 0] * sin), -1).flatten(-2).to(tensor.dtype)
q, k = rotate(q), rotate(k)
if c.qk_norm:
# Native Q/K normalization uses dtype-dependent default epsilon.
q = F.rms_norm(q, (c.head_dim,))
k = F.rms_norm(k, (c.head_dim,))
q, k, v = (t.transpose(1, 2) for t in (q, k, v))
if cache is not None:
k, v = cache.update(k, v, effective_layer)
causal = mask is None and q.shape[-2] == k.shape[-2]
if mask is None and q.shape[-2] != k.shape[-2] and q.shape[-2] != 1:
rows = torch.arange(k.shape[-2] - q.shape[-2], k.shape[-2], device=x.device)
mask = torch.arange(k.shape[-2], device=x.device)[None, :] <= rows[:, None]
y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask,
is_causal=causal, enable_gqa=c.num_attention_heads != c.num_key_value_heads,
dropout_p=0.0)
return self.c_proj(y.transpose(1, 2).contiguous().view(batch, length, c.hidden_size))
class MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.fc = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False)
self.proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
def forward(self, x):
gate, value = self.fc(x).chunk(2, dim=-1)
return self.proj(F.silu(gate) * value)
class ARLoopBlock(nn.Module):
def __init__(self, config, layer):
super().__init__()
self.attn_norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
self.attn = Attention(config, layer)
self.mlp_norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
self.mlp = MLP(config)
class ARLoopForCausalLM(PreTrainedModel, GenerationMixin):
config_class = ARLoopConfig
base_model_prefix = ""
main_input_name = "input_ids"
_no_split_modules = ["ARLoopBlock"]
_supports_cache_class = True
def __init__(self, config):
super().__init__(config)
self.transformer = nn.ModuleDict({
"wte": nn.Embedding(config.vocab_size, config.hidden_size),
"h": nn.ModuleList(ARLoopBlock(config, i) for i in range(config.physical_layers)),
"ln_f": RMSNorm(config.hidden_size, config.rms_norm_eps),
})
self.shared_value_embedding = nn.Embedding(config.vocab_size,
config.num_key_value_heads * config.head_dim)
self.attnres_queries = nn.Parameter(torch.zeros(2 * config.num_hidden_layers, config.hidden_size))
self.attnres_norms = nn.ModuleList(RMSNorm(config.hidden_size, config.rms_norm_eps)
for _ in range(2 * config.num_hidden_layers))
self.final_attnres_query = nn.Parameter(torch.zeros(config.hidden_size))
self.final_attnres_norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
self.post_init()
def _init_weights(self, module):
if isinstance(module, (nn.Linear, nn.Embedding)):
nn.init.normal_(module.weight, std=0.02)
elif isinstance(module, RMSNorm):
nn.init.ones_(module.weight)
def get_input_embeddings(self):
return self.transformer.wte
def set_input_embeddings(self, value):
self.transformer.wte = value
def _route(self, query, norm, blocks, partial, output_norm=None):
values = blocks + [partial]
if len(values) > self.config.sw_blocks + 1:
values = [values[0], *values[-self.config.sw_blocks:]]
# FLA accumulates routing scores, softmax, and the fused output norm in
# FP32, rounding only the final result back to the residual dtype.
with torch.autocast(device_type=partial.device.type, enabled=False):
stacked = torch.stack(values, dim=-2).float()
scores = (stacked * (query.float() * norm.weight.float())).sum(-1)
scores = scores * torch.rsqrt(stacked.square().mean(-1) + norm.eps)
weights = scores.softmax(dim=-1)
y = torch.zeros_like(partial, dtype=torch.float32)
for i, value in enumerate(values):
y = y + weights[..., i, None] * value.float()
if output_norm is not None:
y = y * torch.rsqrt(y.square().mean(-1, keepdim=True) + norm.eps)
y = y * output_norm.weight.float()
return y.to(partial.dtype)
def forward(self, input_ids=None, attention_mask=None, position_ids=None,
past_key_values=None, inputs_embeds=None, labels=None, use_cache=None,
return_dict=None, output_hidden_states=False, output_attentions=False,
cache_position=None, logits_to_keep=0, **kwargs):
if input_ids is None or inputs_embeds is not None:
raise ValueError("ARLoop requires input_ids for token-dependent value embeddings")
if output_attentions:
raise ValueError("output_attentions is not implemented")
use_cache = self.config.use_cache if use_cache is None else use_cache
if labels is not None:
use_cache = False
if past_key_values is not None and not use_cache:
raise ValueError("past_key_values requires use_cache=True")
cache = past_key_values if use_cache else None
if use_cache and cache is None:
cache = DynamicCache()
past_length = cache.get_seq_length() if cache is not None else 0
batch, length = input_ids.shape
total = past_length + length
if total > self.config.max_position_embeddings:
raise ValueError("Input and cache exceed max_position_embeddings")
if position_ids is None:
if attention_mask is None:
position_ids = torch.arange(past_length, total, device=input_ids.device)[None, :]
else:
position_ids = (attention_mask.long().cumsum(-1) - 1).clamp_min(0)[:, -length:]
key_indices = torch.arange(total, device=input_ids.device)
query_indices = torch.arange(past_length, total, device=input_ids.device)
mask = None
if attention_mask is not None:
if attention_mask.shape != (batch, total):
raise ValueError("attention_mask must cover both cached and new tokens")
if not bool(attention_mask.all()):
mask = (key_indices[None, :] <= query_indices[:, None])[None, None, :, :]
mask = mask & attention_mask[:, None, None, :].bool()
# Use BF16 activations; parameters follow the requested loading dtype.
with torch.autocast(device_type=input_ids.device.type, dtype=torch.bfloat16):
embedded = (self.transformer.wte(input_ids) * self.config.embedding_scale).to(torch.bfloat16)
ve = self.shared_value_embedding(input_ids)
blocks, partial = [embedded], embedded
states = [embedded] if output_hidden_states else None
for loop in range(self.config.loop_count):
for i, block in enumerate(self.transformer.h):
effective = loop * self.config.physical_layers + i
h = self._route(self.attnres_queries[2 * effective],
self.attnres_norms[2 * effective], blocks, partial, block.attn_norm)
if effective > 0 and effective % (self.config.attnres_block_size // 2) == 0:
blocks.append(partial)
partial = None
a = block.attn(h, position_ids, mask, ve, cache, effective)
partial = a if partial is None else partial + a
h = self._route(self.attnres_queries[2 * effective + 1],
self.attnres_norms[2 * effective + 1], blocks, partial, block.mlp_norm)
partial = partial + block.mlp(h)
if states is not None:
states.append(partial)
h = self._route(self.final_attnres_query, self.final_attnres_norm, blocks, partial)
# Native RMSNorm disables autocast around the final normalization.
with torch.autocast(device_type=input_ids.device.type, enabled=False):
h = self.transformer.ln_f(h)
if logits_to_keep and labels is None:
h = h[:, -logits_to_keep:, :]
logits = F.linear(h, self.transformer.wte.weight).float()
if self.config.logit_softcap is not None:
cap = self.config.logit_softcap
logits = cap * torch.tanh(logits / cap)
loss = None
if labels is not None:
loss = F.cross_entropy(logits[:, :-1].contiguous().view(-1, logits.size(-1)),
labels[:, 1:].contiguous().view(-1), ignore_index=-100)
result = CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=cache,
hidden_states=tuple(states) if states is not None else None)
return result if return_dict is not False else result.to_tuple()
def prepare_inputs_for_generation(self, input_ids, past_key_values=None,
attention_mask=None, **kwargs):
if past_key_values is not None:
seen = past_key_values.get_seq_length()
if seen:
input_ids = input_ids[:, seen:] if input_ids.shape[1] > seen else input_ids[:, -1:]
return dict(input_ids=input_ids.contiguous(), past_key_values=past_key_values,
attention_mask=attention_mask, use_cache=kwargs.get("use_cache", True),
logits_to_keep=1)
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