File size: 10,373 Bytes
0f70a95 | 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 | """Portable PyTorch Weevil, with fp32 recurrence and fixed-size generation caches.
This reference implementation needs only PyTorch and Transformers, including for
the latent-QKV variant. Both embedding factors are tied to the vocabulary readout.
"""
from typing import ClassVar
import torch
from torch import nn
from torch.nn import functional as F
from transformers import GenerationMixin, PreTrainedModel
from transformers.cache_utils import Cache
from transformers.modeling_outputs import CausalLMOutputWithPast
from .configuration_weevil import WeevilConfig
class WeevilCache(Cache):
def __init__(self, states=None, seen_tokens=0):
super().__init__(layers=[])
self.states = states or []
self.seen_tokens = seen_tokens
def get_seq_length(self, layer_idx=0):
return self.seen_tokens
def reorder_cache(self, beam_idx):
self.states = [tuple(x.index_select(0, beam_idx.to(x.device)) for x in state)
for state in self.states]
def batch_repeat_interleave(self, repeats):
self.states = [tuple(x.repeat_interleave(repeats, dim=0) for x in state)
for state in self.states]
class ZCRMSNorm(nn.Module):
def __init__(self, width, eps):
super().__init__()
self.scale = nn.Parameter(torch.zeros(width))
self.eps = eps
def forward(self, x):
x = x.float()
return (1 + self.scale.float()) * x * torch.rsqrt(x.square().mean(-1, keepdim=True) + self.eps)
class TiedEmbedding(nn.Module):
def __init__(self, c):
super().__init__()
self.embed = nn.Embedding(c.vocab_size, c.embedding_dim)
self.proj = nn.Linear(c.embedding_dim, c.hidden_size, bias=False)
def forward(self, ids):
return self.proj(self.embed(ids))
def token_logits(self, x):
x = F.linear(x.to(self.proj.weight.dtype), self.proj.weight.T)
return F.linear(x, self.embed.weight).float()
class CausalConv(nn.Module):
def __init__(self, channels, kernel):
super().__init__()
self.weight = nn.Parameter(torch.empty(channels, kernel))
nn.init.normal_(self.weight, std=0.02)
def step(self, x, history, valid):
taps = torch.cat((history, x.float().unsqueeze(1)), dim=1)
output = (taps * self.weight.float().T).sum(1)
history = torch.where(valid[:, None, None], taps[:, 1:], history)
return F.silu(output.to(x.dtype)), history
class GDN2Mixer(nn.Module):
def __init__(self, c):
super().__init__()
self.config = c
hidden, head = c.hidden_size, c.hidden_size // c.num_heads
width = c.num_heads * head
def linear(i, o, bias=False):
return nn.Linear(i, o, bias=bias)
if c.qkv_rank is None:
self.q_proj, self.k_proj, self.v_proj = [linear(hidden, width) for _ in range(3)]
else:
self.qkv_a = linear(hidden, c.qkv_rank)
self.q_b, self.k_b, self.v_b = [linear(c.qkv_rank, width) for _ in range(3)]
self.q_conv, self.k_conv, self.v_conv = [CausalConv(width, c.conv_kernel) for _ in range(3)]
self.f_in, self.f_out = linear(hidden, head), linear(head, width)
self.b_proj, self.w_proj = linear(hidden, width), linear(hidden, width)
self.g_in, self.g_out = linear(hidden, head), linear(head, width, bias=True)
self.o_proj = linear(width, hidden)
self.o_norm = nn.Parameter(torch.ones(head))
self.A_log = nn.Parameter(torch.zeros(c.num_heads))
self.dt_bias = nn.Parameter(torch.zeros(width))
def forward(self, x, valid, state=None):
c = self.config
batch, length, _ = x.shape
head = c.hidden_size // c.num_heads
width = c.num_heads * head
if state is None:
recurrent = torch.zeros(batch, c.num_heads, head, head, device=x.device)
histories = [torch.zeros(batch, c.conv_kernel - 1, width, device=x.device) for _ in range(3)]
else:
recurrent, *histories = state
if c.qkv_rank is None:
projections = [self.q_proj(x), self.k_proj(x), self.v_proj(x)]
else:
latent = self.qkv_a(x)
projections = [self.q_b(latent), self.k_b(latent), self.v_b(latent)]
shape = (batch, length, c.num_heads, head)
decay = -self.A_log.float().exp()[None, None, :, None] * F.softplus(
self.f_out(self.f_in(x)).float() + self.dt_bias.float()).reshape(shape)
erase = self.b_proj(x).sigmoid().float().reshape(shape)
write = self.w_proj(x).sigmoid().float().reshape(shape)
outputs = []
for t in range(length):
active = valid[:, t]
qkv = []
for i, conv in enumerate((self.q_conv, self.k_conv, self.v_conv)):
value, histories[i] = conv.step(projections[i][:, t], histories[i], active)
qkv.append(value.float().reshape(batch, c.num_heads, head))
q, k, v = qkv
q = q * torch.rsqrt(q.square().sum(-1, keepdim=True) + 1e-6) * head ** -0.5
k = k * torch.rsqrt(k.square().sum(-1, keepdim=True) + 1e-6)
decayed = recurrent * decay[:, t].exp().unsqueeze(-1)
erased = ((erase[:, t] * k).unsqueeze(-1) * decayed).sum(-2)
updated = decayed + k.unsqueeze(-1) * (write[:, t] * v - erased).unsqueeze(-2)
recurrent = torch.where(active[:, None, None, None], updated, recurrent)
outputs.append((q.unsqueeze(-1) * recurrent).sum(-2))
output = torch.stack(outputs, dim=1)
output = output * torch.rsqrt(output.square().mean(-1, keepdim=True) + c.norm_eps)
gate = self.g_out(self.g_in(x)).float().reshape(shape).sigmoid()
output = output * self.o_norm.float() * gate
output = self.o_proj(output.flatten(-2).to(x.dtype))
return output.masked_fill(~valid[..., None], 0), (recurrent, *histories)
class SwiGLU(nn.Module):
def __init__(self, c):
super().__init__()
self.gate_proj = nn.Linear(c.hidden_size, c.intermediate_size, bias=False)
self.up_proj = nn.Linear(c.hidden_size, c.intermediate_size, bias=False)
self.down_proj = nn.Linear(c.intermediate_size, c.hidden_size, bias=False)
def forward(self, x):
return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
class WeevilLayer(nn.Module):
def __init__(self, c):
super().__init__()
self.norm = ZCRMSNorm(c.hidden_size, c.norm_eps)
self.mixer = GDN2Mixer(c)
self.mlp = SwiGLU(c)
def forward(self, x, valid, state):
z = self.norm(x).to(self.mixer.o_proj.weight.dtype)
mixed, state = self.mixer(z, valid, state)
x = x.float() + mixed.float() + self.mlp(z).float()
return x.masked_fill(~valid[..., None], 0), state
class WeevilForCausalLM(PreTrainedModel, GenerationMixin):
config_class = WeevilConfig
base_model_prefix = "weevil"
main_input_name = "input_ids"
_no_split_modules: ClassVar[list[str]] = ["WeevilLayer"]
def __init__(self, config):
super().__init__(config)
self.embedding = TiedEmbedding(config)
self.decoder = nn.ModuleDict({"layers": nn.ModuleList([
WeevilLayer(config) for _ in range(config.num_hidden_layers)
])})
self.final_norm = ZCRMSNorm(config.hidden_size, config.norm_eps)
self.post_init()
@classmethod
def _supports_default_dynamic_cache(cls):
return False
def get_input_embeddings(self):
return self.embedding.embed
def set_input_embeddings(self, value):
self.embedding.embed = value
def prepare_inputs_for_generation(self, input_ids, past_key_values=None,
attention_mask=None, use_cache=True, **kwargs):
if past_key_values is not None and past_key_values.get_seq_length():
input_ids = input_ids[:, past_key_values.get_seq_length():]
return {"input_ids": input_ids, "attention_mask": attention_mask,
"past_key_values": past_key_values, "use_cache": use_cache}
def forward(self, input_ids=None, attention_mask=None, past_key_values=None,
inputs_embeds=None, labels=None, use_cache=None, return_dict=None,
token_type_ids=None, **kwargs):
if (input_ids is None) == (inputs_embeds is None):
raise ValueError("Provide exactly one of input_ids or inputs_embeds")
x = self.embedding(input_ids) if inputs_embeds is None else inputs_embeds
batch, length = x.shape[:2]
valid = (torch.ones(batch, length, dtype=torch.bool, device=x.device)
if attention_mask is None else attention_mask[:, -length:].to(device=x.device, dtype=torch.bool))
x = x.float().masked_fill(~valid[..., None], 0)
use_cache = self.config.use_cache if use_cache is None else use_cache
if past_key_values is not None and not isinstance(past_key_values, WeevilCache):
raise ValueError("Weevil requires a WeevilCache, not an attention KV cache")
states = []
for i, layer in enumerate(self.decoder["layers"]):
state = past_key_values.states[i] if past_key_values is not None and past_key_values.states else None
x, state = layer(x, valid, state)
states.append(state)
logits = self.embedding.token_logits(self.final_norm(x))
logits[..., self.config.pad_token_id] = -1e9
logits = logits.masked_fill(~valid[..., None], 0)
loss = None
if labels is not None:
targets = labels[:, 1:].to(logits.device).clone()
targets.masked_fill_(~valid[:, 1:], -100)
loss = F.cross_entropy(logits[:, :-1].reshape(-1, self.config.vocab_size),
targets.reshape(-1), ignore_index=-100)
seen = 0 if past_key_values is None else past_key_values.get_seq_length()
cache = WeevilCache(states, seen + length) if use_cache else None
if return_dict is False:
result = (logits, cache) if use_cache else (logits,)
return (loss, *result) if loss is not None else result
return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=cache)
WeevilForCausalLM.register_for_auto_class("AutoModelForCausalLM")
|