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| # Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright: | |
| 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, | |
| past_key_value: Optional[Tuple[torch.Tensor]] = None, | |
| attention_mask: Optional[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] | |
| offset = 0 | |
| if past_key_value is not None: | |
| offset = past_key_value[0].shape[-2] | |
| kv_seq_len += offset | |
| 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, | |
| offset=offset) | |
| # [bsz, nh, t, hd] | |
| assert not output_attentions, "output_attentions is not supported" | |
| assert not use_cache, "use_cache is not supported" | |
| assert past_key_value is None, "past_key_value 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 | |