103 lines
4.3 KiB
Python
103 lines
4.3 KiB
Python
# Adopted from https://github.com/lm-sys/FastChat. Below is the original copyright:
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from typing import List, Optional, Tuple
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import torch
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from torch import nn
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import transformers
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from transformers.models.llama.modeling_llama import apply_rotary_pos_emb
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from einops import rearrange
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from flash_attn.flash_attn_interface import flash_attn_unpadded_qkvpacked_func
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from flash_attn.bert_padding import unpad_input, pad_input
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def forward(
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self,
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hidden_states: torch.Tensor,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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attention_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor],
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Optional[Tuple[torch.Tensor]]]:
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"""Input shape: Batch x Time x Channel
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attention_mask: [bsz, q_len]
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"""
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states).view(
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bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = self.k_proj(hidden_states).view(
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bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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value_states = self.v_proj(hidden_states).view(
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bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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# [bsz, q_len, nh, hd]
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# [bsz, nh, q_len, hd]
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kv_seq_len = key_states.shape[-2]
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offset = 0
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if past_key_value is not None:
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offset = past_key_value[0].shape[-2]
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kv_seq_len += offset
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cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
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query_states, key_states = apply_rotary_pos_emb(query_states,
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key_states,
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cos,
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sin,
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offset=offset)
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# [bsz, nh, t, hd]
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assert not output_attentions, "output_attentions is not supported"
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assert not use_cache, "use_cache is not supported"
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assert past_key_value is None, "past_key_value is not supported"
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# Flash attention codes from
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# https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/flash_attention.py
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# transform the data into the format required by flash attention
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qkv = torch.stack([query_states, key_states, value_states], dim=2) # [bsz, nh, 3, q_len, hd]
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qkv = qkv.transpose(1, 3) # [bsz, q_len, 3, nh, hd]
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# We have disabled _prepare_decoder_attention_mask in LlamaModel
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# the attention_mask should be the same as the key_padding_mask
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key_padding_mask = attention_mask
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if key_padding_mask is None:
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qkv = rearrange(qkv, 'b s ... -> (b s) ...')
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max_s = q_len
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cu_q_lens = torch.arange(0, (bsz + 1) * q_len, step=q_len, dtype=torch.int32,
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device=qkv.device)
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output = flash_attn_unpadded_qkvpacked_func(
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qkv, cu_q_lens, max_s, 0.0,
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softmax_scale=None, causal=True
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)
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output = rearrange(output, '(b s) ... -> b s ...', b=bsz)
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else:
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nheads = qkv.shape[-2]
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x = rearrange(qkv, 'b s three h d -> b s (three h d)')
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x_unpad, indices, cu_q_lens, max_s = unpad_input(x, key_padding_mask)
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x_unpad = rearrange(x_unpad, 'nnz (three h d) -> nnz three h d', three=3, h=nheads)
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output_unpad = flash_attn_unpadded_qkvpacked_func(
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x_unpad, cu_q_lens, max_s, 0.0,
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softmax_scale=None, causal=True
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)
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output = rearrange(pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'),
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indices, bsz, q_len),
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'b s (h d) -> b s h d', h=nheads)
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return self.o_proj(rearrange(output,
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'b s h d -> b s (h d)')), None, None
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# Disable the transformation of the attention mask in LlamaModel as the flash attention
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# requires the attention mask to be the same as the key_padding_mask
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def _prepare_decoder_attention_mask(self, attention_mask, input_shape,
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inputs_embeds, past_key_values_length):
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# [bsz, seq_len]
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return attention_mask
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def replace_llama_attn_with_flash_attn():
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transformers.models.llama.modeling_llama.LlamaModel._prepare_decoder_attention_mask = _prepare_decoder_attention_mask
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transformers.models.llama.modeling_llama.LlamaAttention.forward = forward
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