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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) | |