from typing import List, Optional, Tuple import torch from torch import nn import transformers from transformers.models.llama.modeling_llama import apply_rotary_pos_emb from einops import rearrange from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func from flash_attn.bert_padding import unpad_input, pad_input def forward( self, hidden_states: 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, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel attention_mask: [bsz, q_len] """ 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) .view(bsz, q_len, self.num_heads, self.head_dim) .transpose(1, 2) ) value_states = ( self.v_proj(hidden_states) .view(bsz, q_len, self.num_heads, self.head_dim) .transpose(1, 2) ) # [bsz, q_len, nh, hd] # [bsz, nh, q_len, hd] kv_seq_len = key_states.shape[-2] assert past_key_value is None, "past_key_value is not supported" 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 ) # [bsz, nh, t, hd] assert not output_attentions, "output_attentions is not supported" assert not use_cache, "use_cache is not supported" # Flash attention codes from # https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py # transform the data into the format required by flash attention qkv = torch.stack( [query_states, key_states, value_states], dim=2 ) # [bsz, nh, 3, q_len, hd] qkv = qkv.transpose(1, 3) # [bsz, q_len, 3, nh, hd] # We have disabled _prepare_decoder_attention_mask in LlamaModel # the attention_mask should be the same as the key_padding_mask key_padding_mask = attention_mask if key_padding_mask is None: qkv = rearrange(qkv, "b s ... -> (b s) ...") max_s = q_len cu_q_lens = torch.arange( 0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32, device=qkv.device ) output = flash_attn_unpadded_qkvpacked_func( qkv, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True ) output = rearrange(output, "(b s) ... -> b s ...", b=bsz) else: nheads = qkv.shape[-2] x = rearrange(qkv, "b s three h d -> b s (three h d)") x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask) x_unpad = rearrange( x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads ) output_unpad = flash_attn_unpadded_qkvpacked_func( x_unpad, cu_q_lens, max_s, 0.0, softmax_scale=None, causal=True ) output = rearrange( pad_input( rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, bsz, q_len ), "b s (h d) -> b s h d", h=nheads, ) return self.o_proj(rearrange(output, "b s h d -> b s (h d)")), None, None # 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(): transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = ( _prepare_decoder_attention_mask ) transformers.models.llama.modeling_llama.LlamaAttention.forward = forward