import torch import torch.nn as nn import transformers class CustomQwen2Decoder(nn.Module): """ Qwen2 visual encoder non-causal attention + causal attention token_type_ids ：0=non-causal, 1=causal """ def __init__( self, decoder_layer: int = 24, max_position_embeddings: int = 131072, hidden_dimension: int = 896, num_attention_heads: int = 14, num_key_value_heads: int = 2, intermediate_size: int = 4864, vocab_size: int = 151936, attn_implementation: str = "sdpa", # ⭐ rms_norm_eps: float = 1e-06, rope_theta: float = 1000000.0, attention_dropout: float = 0.0, hidden_act: str = "silu", initializer_range: float = 0.02, ): super().__init__() # attn_implementation check if attn_implementation == "flash_attention_2": raise ValueError( "CustomQwen2Decoder do not support flash_attention_2，" "new attention mask needs 'sdpa' or 'eager'" ) # load Qwen2Model = getattr(transformers.models.qwen2.modeling_qwen2, 'Qwen2Model') Qwen2Config = getattr(transformers, 'Qwen2Config') # config config = Qwen2Config( hidden_size=hidden_dimension, num_hidden_layers=decoder_layer, num_attention_heads=num_attention_heads, num_key_value_heads=num_key_value_heads, intermediate_size=intermediate_size, max_position_embeddings=max_position_embeddings, vocab_size=vocab_size, rms_norm_eps=rms_norm_eps, rope_theta=rope_theta, attention_dropout=attention_dropout, hidden_act=hidden_act, initializer_range=initializer_range, _attn_implementation=attn_implementation, # ⭐ ) # self.model = self._create_custom_model(Qwen2Model, config) del self.model.embed_tokens def _create_custom_model(self, Qwen2Model, config): """ Qwen2Model """ class CustomQwen2ModelInner(Qwen2Model): def forward( self, input_ids=None, attention_mask=None, position_ids=None, past_key_values=None, inputs_embeds=None, token_type_ids=None, # ⭐ use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, cache_position=None, ): # token_type_ids self._current_token_type_ids = token_type_ids outputs = super().forward( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, cache_position=cache_position, ) return outputs def _update_causal_mask( self, attention_mask, input_tensor, cache_position, past_key_values, output_attentions, ): dtype, device = input_tensor.dtype, input_tensor.device min_dtype = torch.finfo(dtype).min batch_size, sequence_length = input_tensor.shape[0], input_tensor.shape[1] token_type_ids = self._current_token_type_ids # attention mask causal_mask = self._create_custom_4d_mask( sequence_length=sequence_length, dtype=dtype, device=device, batch_size=batch_size, token_type_ids=token_type_ids, ) # padding mask if attention_mask is not None and attention_mask.dim() == 2: padding_mask = attention_mask[:, None, None, :].to(dtype=dtype) padding_mask = (1.0 - padding_mask) * min_dtype causal_mask = causal_mask + padding_mask return causal_mask def _create_custom_4d_mask( self, sequence_length, dtype, device, batch_size, token_type_ids, ): min_dtype = torch.finfo(dtype).min masks = [] for b in range(batch_size): mask = torch.full( (sequence_length, sequence_length), fill_value=min_dtype, dtype=dtype, device=device ) type_ids = token_type_ids[b] image_positions = (type_ids == 0).nonzero(as_tuple=True)[0] text_positions = (type_ids == 1).nonzero(as_tuple=True)[0] # non-casual if len(image_positions) > 0: mask[image_positions[:, None], image_positions] = 0.0 # causal for i, text_pos in enumerate(text_positions): if len(image_positions) > 0: mask[text_pos, image_positions] = 0.0 mask[text_pos, text_positions[:i+1]] = 0.0 masks.append(mask) mask = torch.stack(masks, dim=0).unsqueeze(1) return mask return CustomQwen2ModelInner(config) def forward( self, inputs_embeds, token_type_ids, attention_mask=None, **kwargs ): """ Args: inputs_embeds: [batch_size, seq_len, hidden_dim] token_type_ids: [batch_size, seq_len], 0=non-causal, 1=causal attention_mask: [batch_size, seq_len], optional """ return self.model( inputs_embeds=inputs_embeds, token_type_ids=token_type_ids, attention_mask=attention_mask, **kwargs ) # batch_size = 2 # inputs_embeds = torch.randn(batch_size, 512, 896).cuda() # inputs_embeds = torch.randn(batch_size, 512, 896).cuda() # token_type_ids = torch.cat([ # torch.zeros(batch_size, 256, dtype=torch.long), # torch.ones(batch_size, 256, dtype=torch.long), # ], dim=1).cuda() # # start = time.time() # with torch.no_grad(): # outputs_sdpa = decoder_sdpa(inputs_embeds, token_type_ids) # print(outputs_sdpa[0].shape) # print(f"SDPA time: {time.time() - start:.4f}s") class Qwen2Decoder2Encoder(nn.Module): """ Decoder based on Multilingual BART Set the initial weights and configuration with a pretrained multilingual BART model, and modify the detailed configurations as a Nougat decoder """ def __init__( self, decoder_layer: int, hidden_dimension: int, num_attention_heads: int, num_key_value_heads: int, intermediate_size: int, max_query: int, ): super().__init__() self.model = CustomQwen2Decoder( decoder_layer=decoder_layer, hidden_dimension=hidden_dimension, num_attention_heads=num_attention_heads, num_key_value_heads=num_key_value_heads, intermediate_size=intermediate_size, attn_implementation="sdpa", ) self.query_768 = nn.Embedding(144, hidden_dimension) self.query_1024 = nn.Embedding(256, hidden_dimension) # self.query_refixation = nn.Embedding(int(math.sqrt(max_query)), hidden_dimension) def forward(self, x: torch.Tensor) -> torch.Tensor: x = x.flatten(2).transpose(1, 2) bs, n_query, _ = x.shape if n_query == 144: param_img = self.query_768.weight elif n_query == 256: param_img = self.query_1024.weight batch_query_imgs = param_img.unsqueeze(0).expand( bs, -1, -1 ) # (batch_size, num_queries, hidden_size) x_combined = torch.cat([x, batch_query_imgs], dim=1) token_type_ids = torch.cat([ torch.zeros(bs, n_query, dtype=torch.long), torch.ones(bs, n_query, dtype=torch.long), ], dim=1) y = self.model(x_combined, token_type_ids)[0] y = y[:, n_query:, :] # causal flow query return y def build_qwen2_decoder_as_encoder( decoder_layer=24, hidden_dimension=896, num_attention_heads=14, num_key_value_heads=2, intermediate_size=4864, max_query = 400, checkpoint=None, ): decoder_as_encoder = Qwen2Decoder2Encoder( decoder_layer=decoder_layer, hidden_dimension = hidden_dimension, num_attention_heads = num_attention_heads, num_key_value_heads = num_key_value_heads, intermediate_size = intermediate_size, max_query = max_query ) if checkpoint is not None: # with open(checkpoint, "rb") as f: state_dict = torch.load(checkpoint) decoder_as_encoder.load_state_dict(state_dict, strict=True) # tob print(checkpoint) return decoder_as_encoder if __name__ == '__main__': x = torch.zeros(2, 896, 16, 16).cuda() net = build_qwen2_decoder_as_encoder(checkpoint = '').cuda() y = net(x) # y = y.flatten(2).permute(0, 2, 1) print('-------shape---------') print(y.shape) print('-------------------')