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import torch
import math
from backend.attention import attention_pytorch as attention_function
activations = {
"gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"),
"relu": torch.nn.functional.relu,
}
class T5LayerNorm(torch.nn.Module):
def __init__(self, hidden_size, eps=1e-6):
super().__init__()
self.weight = torch.nn.Parameter(torch.empty(hidden_size))
self.variance_epsilon = eps
def forward(self, x):
variance = x.pow(2).mean(-1, keepdim=True)
x = x * torch.rsqrt(variance + self.variance_epsilon)
return self.weight.to(x) * x
class T5DenseActDense(torch.nn.Module):
def __init__(self, model_dim, ff_dim, ff_activation):
super().__init__()
self.wi = torch.nn.Linear(model_dim, ff_dim, bias=False)
self.wo = torch.nn.Linear(ff_dim, model_dim, bias=False)
self.act = activations[ff_activation]
def forward(self, x):
x = self.act(self.wi(x))
x = self.wo(x)
return x
class T5DenseGatedActDense(torch.nn.Module):
def __init__(self, model_dim, ff_dim, ff_activation):
super().__init__()
self.wi_0 = torch.nn.Linear(model_dim, ff_dim, bias=False)
self.wi_1 = torch.nn.Linear(model_dim, ff_dim, bias=False)
self.wo = torch.nn.Linear(ff_dim, model_dim, bias=False)
self.act = activations[ff_activation]
def forward(self, x):
hidden_gelu = self.act(self.wi_0(x))
hidden_linear = self.wi_1(x)
x = hidden_gelu * hidden_linear
x = self.wo(x)
return x
class T5LayerFF(torch.nn.Module):
def __init__(self, model_dim, ff_dim, ff_activation, gated_act):
super().__init__()
if gated_act:
self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, ff_activation)
else:
self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, ff_activation)
self.layer_norm = T5LayerNorm(model_dim)
def forward(self, x):
forwarded_states = self.layer_norm(x)
forwarded_states = self.DenseReluDense(forwarded_states)
x += forwarded_states
return x
class T5Attention(torch.nn.Module):
def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias):
super().__init__()
self.q = torch.nn.Linear(model_dim, inner_dim, bias=False)
self.k = torch.nn.Linear(model_dim, inner_dim, bias=False)
self.v = torch.nn.Linear(model_dim, inner_dim, bias=False)
self.o = torch.nn.Linear(inner_dim, model_dim, bias=False)
self.num_heads = num_heads
self.relative_attention_bias = None
if relative_attention_bias:
self.relative_attention_num_buckets = 32
self.relative_attention_max_distance = 128
self.relative_attention_bias = torch.nn.Embedding(self.relative_attention_num_buckets, self.num_heads)
@staticmethod
def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
relative_buckets = 0
if bidirectional:
num_buckets //= 2
relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
relative_position = torch.abs(relative_position)
else:
relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
max_exact = num_buckets // 2
is_small = relative_position < max_exact
relative_position_if_large = max_exact + (
torch.log(relative_position.float() / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact)
).to(torch.long)
relative_position_if_large = torch.min(
relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
)
relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
return relative_buckets
def compute_bias(self, query_length, key_length, device, dtype):
context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
relative_position = memory_position - context_position
relative_position_bucket = self._relative_position_bucket(
relative_position,
bidirectional=True,
num_buckets=self.relative_attention_num_buckets,
max_distance=self.relative_attention_max_distance,
)
values = self.relative_attention_bias(relative_position_bucket).to(dtype)
values = values.permute([2, 0, 1]).unsqueeze(0)
return values
def forward(self, x, mask=None, past_bias=None):
q = self.q(x)
k = self.k(x)
v = self.v(x)
if self.relative_attention_bias is not None:
past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device, x.dtype)
if past_bias is not None:
if mask is not None:
mask = mask + past_bias
else:
mask = past_bias
out = attention_function(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask)
return self.o(out), past_bias
class T5LayerSelfAttention(torch.nn.Module):
def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias):
super().__init__()
self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias)
self.layer_norm = T5LayerNorm(model_dim)
def forward(self, x, mask=None, past_bias=None):
output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias)
x += output
return x, past_bias
class T5Block(torch.nn.Module):
def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias):
super().__init__()
self.layer = torch.nn.ModuleList()
self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias))
self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, gated_act))
def forward(self, x, mask=None, past_bias=None):
x, past_bias = self.layer[0](x, mask, past_bias)
x = self.layer[-1](x)
return x, past_bias
class T5Stack(torch.nn.Module):
def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention):
super().__init__()
self.block = torch.nn.ModuleList(
[T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=((not relative_attention) or (i == 0))) for i in range(num_layers)]
)
self.final_layer_norm = T5LayerNorm(model_dim)
def forward(self, x, attention_mask=None):
mask = None
if attention_mask is not None:
mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
past_bias = None
for i, l in enumerate(self.block):
x, past_bias = l(x, mask, past_bias)
x = self.final_layer_norm(x)
return x
class T5(torch.nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.num_layers = config["num_layers"]
model_dim = config["d_model"]
self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config["d_ff"], config["dense_act_fn"], config["is_gated_act"], config["num_heads"], config["model_type"] != "umt5")
self.shared = torch.nn.Embedding(config["vocab_size"], model_dim)
def forward(self, input_ids, *args, **kwargs):
x = self.shared(input_ids)
x = torch.nan_to_num(x)
return self.encoder(x, *args, **kwargs)
class IntegratedT5(torch.nn.Module):
def __init__(self, config):
super().__init__()
self.transformer = T5(config)
self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
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