from collections.abc import Callable import torch import torch.nn as nn import torch_npu import vllm from transformers import Qwen2Config from transformers.cache_utils import Cache, DynamicCache from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask from transformers.modeling_flash_attention_utils import FlashAttentionKwargs from transformers.modeling_outputs import BaseModelOutputWithPast from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS from transformers.models.qwen2.modeling_qwen2 import ( Qwen2Attention, Qwen2DecoderLayer, Qwen2Model, eager_attention_forward, ) from transformers.processing_utils import Unpack from transformers.utils import TransformersKwargs from vllm.model_executor.models.deepencoder2 import CustomQwen2Decoder class AscendCustomQwen2Decoder(CustomQwen2Decoder): 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__( decoder_layer, max_position_embeddings, hidden_dimension, num_attention_heads, num_key_value_heads, intermediate_size, vocab_size, attn_implementation, rms_norm_eps, rope_theta, attention_dropout, hidden_act, initializer_range, ) # 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_optimized_custom_model(config) del self.model.embed_tokens def _create_optimized_custom_model(self, config): class CustomQwen2ModelInner(AscendQwen2Model): def forward( self, input_ids=None, attention_mask=None, position_ids=None, past_key_values=None, inputs_embeds=None, use_cache=None, cache_position=None, token_type_ids=None, **kwargs: Unpack[TransformersKwargs], ): # token_type_ids self._current_token_type_ids = token_type_ids causal_mask_mapping = { "full_attention": self._create_npu_optimized_mask( attention_mask=attention_mask, input_tensor=inputs_embeds, token_type_ids=token_type_ids, ) } outputs = super().forward( input_ids=input_ids, attention_mask=causal_mask_mapping, position_ids=position_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, use_cache=use_cache, cache_position=cache_position, ) return outputs def _create_npu_optimized_mask( self, attention_mask, input_tensor, token_type_ids, ): """ 4D Mask generation optimized for NPU vector parallel implementation, replacing the original loop implementation """ 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] if token_type_ids is None: return self._create_standard_causal_mask(batch_size, sequence_length, dtype, device, attention_mask) # ========================================== # NPU optimization: vectorized parallel mask generation (replacing loops) # ========================================== # 1. create image token position mask [batch, seq_len] is_image = (token_type_ids == 0).unsqueeze(-1).to(dtype=dtype, device=device) # [batch, seq_len, 1] is_text = (token_type_ids == 1).unsqueeze(-1).to(dtype=dtype, device=device) # [batch, seq_len, 1] # 2. Bidirectional attention (fully connected) between image tokens. # image_attention: [batch, seq_len, seq_len] image_attention = torch.bmm(is_image, is_image.transpose(1, 2)) # 3. Visibility of text tokens to image tokens (full connection) text_to_image = torch.bmm(is_text, is_image.transpose(1, 2)) # 4. Causal attention from text to text. # First, perform matrix multiplication to obtain the text-text relationship pairs of [B, L, L]. text_to_text_base = torch.bmm(is_text, is_text.transpose(1, 2)) # create casual triangular Lower causal_mask = torch.tril(torch.ones((sequence_length, sequence_length), device=device, dtype=dtype)) text_to_text = text_to_text_base * causal_mask.unsqueeze(0) # 5. Merge all attention patterns combined_mask = image_attention + text_to_image + text_to_text combined_mask = (1 - combined_mask) * min_dtype # reverseļ¼š0->min_dtype, 1->0 # 6. Process Padding Mask (attention_mask) if attention_mask is not None: # Ensure that padding_mask is on the same device p_mask = attention_mask.to(device=device, dtype=dtype) if p_mask.dim() == 2: # Extended to [B, 1, 1, L] to adapt to 4D attention. p_mask = (1.0 - p_mask[:, None, None, :]) * min_dtype return combined_mask.unsqueeze(1) + p_mask return combined_mask.unsqueeze(1) def _create_standard_causal_mask(self, batch_size, seq_len, dtype, device, attention_mask): """Standard causal mask (when token_type_ids is None)""" min_dtype = torch.finfo(dtype).min mask = torch.triu(torch.full((seq_len, seq_len), min_dtype, dtype=dtype, device=device), diagonal=1) mask = mask.unsqueeze(0).unsqueeze(0).expand(batch_size, 1, seq_len, seq_len) 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 mask = mask + padding_mask return mask return CustomQwen2ModelInner(config) class AscendQwen2Model(Qwen2Model): def __init__(self, config: Qwen2Config): super().__init__(config) self.layers = nn.ModuleList( [AscendQwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] ) self.norm = AscendQwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, input_ids: torch.LongTensor | None = None, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: Cache | None = None, inputs_embeds: torch.FloatTensor | None = None, use_cache: bool | None = None, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> BaseModelOutputWithPast: if (input_ids is None) ^ (inputs_embeds is not None): raise ValueError("You must specify exactly one of input_ids or inputs_embeds") if inputs_embeds is None: inputs_embeds = self.embed_tokens(input_ids) if use_cache and past_key_values is None: past_key_values = DynamicCache(config=self.config) if cache_position is None: past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 cache_position = torch.arange( past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device ) if position_ids is None: position_ids = cache_position.unsqueeze(0) # It may already have been prepared by e.g. `generate` if not isinstance(causal_mask_mapping := attention_mask, dict): # Prepare mask arguments mask_kwargs = { "config": self.config, "input_embeds": inputs_embeds, "attention_mask": attention_mask, "cache_position": cache_position, "past_key_values": past_key_values, "position_ids": position_ids, } # Create the masks causal_mask_mapping = { "full_attention": create_causal_mask(**mask_kwargs), } # The sliding window alternating layers are not always activated depending on the config if self.has_sliding_layers: causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs) hidden_states = inputs_embeds # create position embeddings to be shared across the decoder layers position_embeddings = self.rotary_emb(hidden_states, position_ids) for decoder_layer in self.layers[: self.config.num_hidden_layers]: hidden_states = decoder_layer( hidden_states, attention_mask=causal_mask_mapping[decoder_layer.attention_type], position_ids=position_ids, past_key_values=past_key_values, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, ) hidden_states = self.norm(hidden_states) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=past_key_values if use_cache else None, ) class AscendQwen2DecoderLayer(Qwen2DecoderLayer): def __init__(self, config: Qwen2Config, layer_idx: int): super().__init__(config, layer_idx) self.self_attn = AscendQwen2Attention(config=config, layer_idx=layer_idx) self.input_layernorm = AscendQwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = AscendQwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor | None = None, position_ids: torch.LongTensor | None = None, past_key_values: Cache | None = None, use_cache: bool | None = False, cache_position: torch.LongTensor | None = None, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, **kwargs: Unpack[TransformersKwargs], ) -> torch.Tensor: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) # Self Attention hidden_states, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, use_cache=use_cache, cache_position=cache_position, position_embeddings=position_embeddings, **kwargs, ) hidden_states = residual + hidden_states # Fully Connected residual = hidden_states hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states return hidden_states def optimized_apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): cos = cos.unsqueeze(unsqueeze_dim) sin = sin.unsqueeze(unsqueeze_dim) q_embed = torch_npu.npu_rotary_mul(q, cos, sin) k_embed = torch_npu.npu_rotary_mul(k, cos, sin) return q_embed, k_embed class AscendQwen2Attention(Qwen2Attention): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: Qwen2Config, layer_idx: int): super().__init__(config, layer_idx) def forward( self, hidden_states: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor], attention_mask: torch.Tensor | None, past_key_values: Cache | None = None, cache_position: torch.LongTensor | None = None, **kwargs: Unpack[FlashAttentionKwargs], ) -> tuple[torch.Tensor, torch.Tensor | None]: input_shape = hidden_states.shape[:-1] hidden_shape = (*input_shape, -1, self.head_dim) query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2) key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2) value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2) cos, sin = position_embeddings query_states, key_states = optimized_apply_rotary_pos_emb(query_states, key_states, cos, sin) if past_key_values is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] attn_output, attn_weights = attention_interface( self, query_states, key_states, value_states, attention_mask, dropout=0.0 if not self.training else self.attention_dropout, scaling=self.scaling, sliding_window=self.sliding_window, # main diff with Llama **kwargs, ) attn_output = attn_output.reshape(*input_shape, -1).contiguous() attn_output = self.o_proj(attn_output) return attn_output, attn_weights class AscendQwen2RMSNorm(nn.Module): def __init__(self, hidden_size, eps: float = 1e-6) -> None: super().__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.variance_epsilon = eps def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: return torch_npu.npu_rms_norm(hidden_states, self.weight, epsilon=self.variance_epsilon)[0] vllm.model_executor.models.deepencoder2.CustomQwen2Decoder = AscendCustomQwen2Decoder