from typing import Optional, Tuple import warnings import torch import transformers from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, repeat_kv try: from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func except ImportError: from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func as flash_attn_unpadded_qkvpacked_func from flash_attn.bert_padding import unpad_input, pad_input def forward( self, hidden_states: torch.Tensor, modality_indicators: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: bool = False, use_cache: bool = False, padding_mask: bool = None, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: if output_attentions: warnings.warn( "Output attentions is not supported for patched `LlamaAttention`, returning `None` instead." ) bsz, q_len, _ = hidden_states.size() query_states = ( self.q_proj(hidden_states) .view(bsz, q_len, self.num_heads, self.head_dim) .transpose(1, 2) ) key_states = ( self.k_proj(hidden_states, modality_indicators) .view(bsz, q_len, self.num_key_value_heads, self.head_dim) .transpose(1, 2) ) value_states = ( self.v_proj(hidden_states, modality_indicators) .view(bsz, q_len, self.num_key_value_heads, self.head_dim) .transpose(1, 2) ) # shape: (b, num_heads, s, head_dim) kv_seq_len = key_states.shape[-2] if past_key_value is not None: kv_seq_len += past_key_value[0].shape[-2] cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) query_states, key_states = apply_rotary_pos_emb( query_states, key_states, cos, sin, position_ids ) if past_key_value is not None: # reuse k, v key_states = torch.cat([past_key_value[0], key_states], dim=2) value_states = torch.cat([past_key_value[1], value_states], dim=2) past_key_value = (key_states, value_states) if use_cache else None # repeat k/v heads if n_kv_heads < n_heads key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) # Transform the data into the format required by flash attention qkv = torch.stack([query_states, key_states, value_states], dim=2) qkv = qkv.transpose(1, 3) # shape: [b, s, 3, num_heads, head_dim] key_padding_mask = attention_mask if key_padding_mask is None: qkv = qkv.reshape(-1, 3, self.num_heads, self.head_dim) cu_q_lens = torch.arange( 0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32, device=qkv.device ) max_s = q_len output = flash_attn_unpadded_qkvpacked_func( qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True ) output = output.view(bsz, q_len, -1) else: qkv = qkv.reshape(bsz, q_len, -1) qkv, indices, cu_q_lens, max_s = unpad_input(qkv, key_padding_mask) qkv = qkv.view(-1, 3, self.num_heads, self.head_dim) output_unpad = flash_attn_unpadded_qkvpacked_func( qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True ) output_unpad = output_unpad.reshape(-1, self.num_heads * self.head_dim) output = pad_input(output_unpad, indices, bsz, q_len) return self.o_proj(output), None, past_key_value # Disable the transformation of the attention mask in LlamaModel as the flash attention # requires the attention mask to be the same as the key_padding_mask def _prepare_decoder_attention_mask( self, attention_mask, input_shape, inputs_embeds, past_key_values_length ): # [bsz, seq_len] return attention_mask def replace_llama_attn_with_flash_attn(): cuda_major, cuda_minor = torch.cuda.get_device_capability() if cuda_major < 8: warnings.warn( "Flash attention is only supported on A100 or H100 GPU during training due to head dim > 64 backward." "ref: https://github.com/HazyResearch/flash-attention/issues/190#issuecomment-1523359593" ) transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = ( _prepare_decoder_attention_mask ) transformers.models.llama.modeling_llama.LlamaAttention.forward = forward