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import torch
import math
from comfy.ldm.modules.attention import optimized_attention_for_device
class T5LayerNorm(torch.nn.Module):
def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None):
super().__init__()
self.weight = torch.nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
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(device=x.device, dtype=x.dtype) * x
class T5DenseActDense(torch.nn.Module):
def __init__(self, model_dim, ff_dim, dtype, device, operations):
super().__init__()
self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
# self.dropout = nn.Dropout(config.dropout_rate)
def forward(self, x):
x = torch.nn.functional.relu(self.wi(x))
# x = self.dropout(x)
x = self.wo(x)
return x
class T5DenseGatedActDense(torch.nn.Module):
def __init__(self, model_dim, ff_dim, dtype, device, operations):
super().__init__()
self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
# self.dropout = nn.Dropout(config.dropout_rate)
def forward(self, x):
hidden_gelu = torch.nn.functional.gelu(self.wi_0(x), approximate="tanh")
hidden_linear = self.wi_1(x)
x = hidden_gelu * hidden_linear
# x = self.dropout(x)
x = self.wo(x)
return x
class T5LayerFF(torch.nn.Module):
def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
super().__init__()
if ff_activation == "gelu_pytorch_tanh":
self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, dtype, device, operations)
elif ff_activation == "relu":
self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, dtype, device, operations)
self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
# self.dropout = nn.Dropout(config.dropout_rate)
def forward(self, x):
forwarded_states = self.layer_norm(x)
forwarded_states = self.DenseReluDense(forwarded_states)
# x = x + self.dropout(forwarded_states)
x += forwarded_states
return x
class T5Attention(torch.nn.Module):
def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations):
super().__init__()
# Mesh TensorFlow initialization to avoid scaling before softmax
self.q = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.k = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.v = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
self.o = operations.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device)
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, device=device)
@staticmethod
def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
"""
Adapted from Mesh Tensorflow:
https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
Translate relative position to a bucket number for relative attention. The relative position is defined as
memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
This should allow for more graceful generalization to longer sequences than the model has been trained on
Args:
relative_position: an int32 Tensor
bidirectional: a boolean - whether the attention is bidirectional
num_buckets: an integer
max_distance: an integer
Returns:
a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
"""
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))
# now relative_position is in the range [0, inf)
# half of the buckets are for exact increments in positions
max_exact = num_buckets // 2
is_small = relative_position < max_exact
# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
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):
"""Compute binned relative position bias"""
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 # shape (query_length, key_length)
relative_position_bucket = self._relative_position_bucket(
relative_position, # shape (query_length, key_length)
bidirectional=True,
num_buckets=self.relative_attention_num_buckets,
max_distance=self.relative_attention_max_distance,
)
values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads)
values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
return values
def forward(self, x, mask=None, past_bias=None, optimized_attention=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)
if past_bias is not None:
if mask is not None:
mask = mask + past_bias
else:
mask = past_bias
out = optimized_attention(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, dtype, device, operations):
super().__init__()
self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations)
self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
# self.dropout = nn.Dropout(config.dropout_rate)
def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
normed_hidden_states = self.layer_norm(x)
output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias, optimized_attention=optimized_attention)
# x = x + self.dropout(attention_output)
x += output
return x, past_bias
class T5Block(torch.nn.Module):
def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias, dtype, device, operations):
super().__init__()
self.layer = torch.nn.ModuleList()
self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations))
self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, dtype, device, operations))
def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention)
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, num_heads, dtype, device, operations):
super().__init__()
self.block = torch.nn.ModuleList(
[T5Block(model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device, operations=operations) for i in range(num_layers)]
)
self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
# self.dropout = nn.Dropout(config.dropout_rate)
def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
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"))
intermediate = None
optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True)
past_bias = None
for i, l in enumerate(self.block):
x, past_bias = l(x, mask, past_bias, optimized_attention)
if i == intermediate_output:
intermediate = x.clone()
x = self.final_layer_norm(x)
if intermediate is not None and final_layer_norm_intermediate:
intermediate = self.final_layer_norm(intermediate)
return x, intermediate
class T5(torch.nn.Module):
def __init__(self, config_dict, dtype, device, operations):
super().__init__()
self.num_layers = config_dict["num_layers"]
model_dim = config_dict["d_model"]
self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["num_heads"], dtype, device, operations)
self.dtype = dtype
self.shared = torch.nn.Embedding(config_dict["vocab_size"], model_dim, device=device)
def get_input_embeddings(self):
return self.shared
def set_input_embeddings(self, embeddings):
self.shared = embeddings
def forward(self, input_ids, *args, **kwargs):
x = self.shared(input_ids)
return self.encoder(x, *args, **kwargs)
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