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import torch
from comfy.ldm.modules.attention import optimized_attention_for_device
import comfy.ops
class BertAttention(torch.nn.Module):
def __init__(self, embed_dim, heads, dtype, device, operations):
super().__init__()
self.heads = heads
self.query = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
self.key = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
self.value = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
def forward(self, x, mask=None, optimized_attention=None):
q = self.query(x)
k = self.key(x)
v = self.value(x)
out = optimized_attention(q, k, v, self.heads, mask)
return out
class BertOutput(torch.nn.Module):
def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations):
super().__init__()
self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device)
self.LayerNorm = operations.LayerNorm(output_dim, eps=layer_norm_eps, dtype=dtype, device=device)
# self.dropout = nn.Dropout(0.0)
def forward(self, x, y):
x = self.dense(x)
# hidden_states = self.dropout(hidden_states)
x = self.LayerNorm(x + y)
return x
class BertAttentionBlock(torch.nn.Module):
def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations):
super().__init__()
self.self = BertAttention(embed_dim, heads, dtype, device, operations)
self.output = BertOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations)
def forward(self, x, mask, optimized_attention):
y = self.self(x, mask, optimized_attention)
return self.output(y, x)
class BertIntermediate(torch.nn.Module):
def __init__(self, embed_dim, intermediate_dim, dtype, device, operations):
super().__init__()
self.dense = operations.Linear(embed_dim, intermediate_dim, dtype=dtype, device=device)
def forward(self, x):
x = self.dense(x)
return torch.nn.functional.gelu(x)
class BertBlock(torch.nn.Module):
def __init__(self, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations):
super().__init__()
self.attention = BertAttentionBlock(embed_dim, heads, layer_norm_eps, dtype, device, operations)
self.intermediate = BertIntermediate(embed_dim, intermediate_dim, dtype, device, operations)
self.output = BertOutput(intermediate_dim, embed_dim, layer_norm_eps, dtype, device, operations)
def forward(self, x, mask, optimized_attention):
x = self.attention(x, mask, optimized_attention)
y = self.intermediate(x)
return self.output(y, x)
class BertEncoder(torch.nn.Module):
def __init__(self, num_layers, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations):
super().__init__()
self.layer = torch.nn.ModuleList([BertBlock(embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations) for i in range(num_layers)])
def forward(self, x, mask=None, intermediate_output=None):
optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
if intermediate_output is not None:
if intermediate_output < 0:
intermediate_output = len(self.layer) + intermediate_output
intermediate = None
for i, l in enumerate(self.layer):
x = l(x, mask, optimized_attention)
if i == intermediate_output:
intermediate = x.clone()
return x, intermediate
class BertEmbeddings(torch.nn.Module):
def __init__(self, vocab_size, max_position_embeddings, type_vocab_size, pad_token_id, embed_dim, layer_norm_eps, dtype, device, operations):
super().__init__()
self.word_embeddings = operations.Embedding(vocab_size, embed_dim, padding_idx=pad_token_id, dtype=dtype, device=device)
self.position_embeddings = operations.Embedding(max_position_embeddings, embed_dim, dtype=dtype, device=device)
self.token_type_embeddings = operations.Embedding(type_vocab_size, embed_dim, dtype=dtype, device=device)
self.LayerNorm = operations.LayerNorm(embed_dim, eps=layer_norm_eps, dtype=dtype, device=device)
def forward(self, input_tokens, token_type_ids=None, dtype=None):
x = self.word_embeddings(input_tokens, out_dtype=dtype)
x += comfy.ops.cast_to_input(self.position_embeddings.weight[:x.shape[1]], x)
if token_type_ids is not None:
x += self.token_type_embeddings(token_type_ids, out_dtype=x.dtype)
else:
x += comfy.ops.cast_to_input(self.token_type_embeddings.weight[0], x)
x = self.LayerNorm(x)
return x
class BertModel_(torch.nn.Module):
def __init__(self, config_dict, dtype, device, operations):
super().__init__()
embed_dim = config_dict["hidden_size"]
layer_norm_eps = config_dict["layer_norm_eps"]
self.embeddings = BertEmbeddings(config_dict["vocab_size"], config_dict["max_position_embeddings"], config_dict["type_vocab_size"], config_dict["pad_token_id"], embed_dim, layer_norm_eps, dtype, device, operations)
self.encoder = BertEncoder(config_dict["num_hidden_layers"], embed_dim, config_dict["intermediate_size"], config_dict["num_attention_heads"], layer_norm_eps, dtype, device, operations)
def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None):
x = self.embeddings(input_tokens, dtype=dtype)
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"))
x, i = self.encoder(x, mask, intermediate_output)
return x, i
class BertModel(torch.nn.Module):
def __init__(self, config_dict, dtype, device, operations):
super().__init__()
self.bert = BertModel_(config_dict, dtype, device, operations)
self.num_layers = config_dict["num_hidden_layers"]
def get_input_embeddings(self):
return self.bert.embeddings.word_embeddings
def set_input_embeddings(self, embeddings):
self.bert.embeddings.word_embeddings = embeddings
def forward(self, *args, **kwargs):
return self.bert(*args, **kwargs)
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